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Improved Deep Forest Mode for Detection of Fraudulent Online Transaction

Mian Huang, Lizhi Wang, Zhaohui Zhang
2020 Computing and informatics  
In this paper, aiming at sample imbalance and strong concealment of online transactions, we enhance the original deep forest framework to propose a deep forest-based online transaction fraud detection  ...  In addition, the autoencoder model is introduced into the detection model to enhance the representation learning ability.  ...  In addition, some research about deep learning (DL) techniques are gradually being used in fraud detection tasks.  ... 
doi:10.31577/cai_2020_5_1082 fatcat:4udkvbwobvc75mrnxioi3ztaku

Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine

Apapan Pumsirirat, Liu Yan
2018 International Journal of Advanced Computer Science and Applications  
So, fraud detection systems need to detect online transactions by using unsupervised learning, because some fraudsters commit frauds once through online mediums and then switch to other techniques.  ...  This paper aims to 1) focus on fraud cases that cannot be detected based on previous history or supervised learning, 2) create a model of deep Auto-encoder and restricted Boltzmann machine (RBM) that can  ...  DEEP LEARNING TECHNIQUE FOR DETECT CREDIT CARD FRAUD Deep learning is the state of the art technology that recently attracted the IT circle's considerable attention.  ... 
doi:10.14569/ijacsa.2018.090103 fatcat:7ryiptvyefbsbhstnz6puzjpc4

Bidirectional gated recurrent unit for improving classification in credit card fraud detection

Imane Sadgali, Nawal Sael, Fouazia Benabbou
2021 Indonesian Journal of Electrical Engineering and Computer Science  
The aim of this level is to develop a classifier for the detection of credit card fraud, using bidirectional gated recurrent units (BGRU).  ...  Therefore, it is mandatory to use techniques that are able to assist in the detection of credit card fraud.  ...  Rushin and al. used deep learning algorithms (auto encoders) in fraud detection and found that deep learning techniques were better than gradient boosted trees and logistic regression [5] .  ... 
doi:10.11591/ijeecs.v21.i3.pp1704-1712 fatcat:7risqazhxrgwbijqiieojjqc3a

An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies using Deep Learning

Wu Xiuguo, Du Shengyong
2022 IEEE Access  
As such, this paper aims to develop an enhanced system for detecting financial fraud using a state-of-the-art deep learning models based on combination of numerical features that derived from financial  ...  Intelligent financial statement fraud detection systems have therefore been developed to support decision-making for the stakeholders.  ...  Also, we are very thankful to the China Stock Market & Accounting Research Database (CSMAR) for providing us with the dataset for conducting various numerical experiments reported in this paper.  ... 
doi:10.1109/access.2022.3153478 fatcat:vig7l3isqraalherky2s5fpiom

Transaction Fraud Detection Using GRU-centered Sandwich-structured Model [article]

Xurui Li, Wei Yu, Tianyu Luwang, Jianbin Zheng, Xuetao Qiu, Jintao Zhao, Lei Xia, Yujiao Li
2018 arXiv   pre-print
In this paper, a new "within->between->within" sandwich-structured sequence learning architecture has been proposed by stacking an ensemble method, a deep sequential learning method and another top-layer  ...  Models in this structure have been manifested to be very efficient in scenarios like fraud detection, where the information sequence is made up of vectors with complex interconnected features.  ...  A new "withinbetweenwithin" (WBW) sandwich-structured sequence learning architecture has been proposed by combining ensemble and deep learning methods.  ... 
arXiv:1711.01434v3 fatcat:r3hd4z3a6ngtzc6wupwowpcdsm

Quantitative Detection of Financial Fraud Based on Deep Learning with Combination of E-Commerce Big Data

Jian Liu, Xin Gu, Chao Shang, M. Irfan Uddin
2020 Complexity  
Therefore, in the context of e-commerce big data, this paper proposes a quantitative detection algorithm for financial fraud based on deep learning.  ...  The detection and prevention of financial frauds are of great significance for regulating and maintaining a reasonable financial order.  ...  Figure 2 : 2 e main ways of financial fraud. Figure 3 : 3 Structure of stacked denoising encoder. Figure 4 : 4 Detection process of financial fraud based on feature learning.  ... 
doi:10.1155/2020/6685888 fatcat:kkzxywyvivacxghm6juxpk4v4m

Deep-Net: Deep Neural Network for Cyber Security Use Cases [article]

Vinayakumar R and Barathi Ganesh HB and Prabaharan Poornachandran and Anand Kumar M and Soman KP
2018 arXiv   pre-print
The efficient network architecture for DNN is chosen by conducting various trails of experiments for network parameters and network structures.  ...  In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.  ...  We are also grateful to NVIDIA India, for the GPU hardware support to research grant. We are grateful to Computational Engineering and Networking (CEN) department for encouraging the research.  ... 
arXiv:1812.03519v1 fatcat:ujvoi2xxdrdmpfbdprfnkkzqzq

A state of the art survey of data mining-based fraud detection and credit scoring

Xun Zhou, Sicong Cheng, Meng Zhu, Chengkun Guo, Sida Zhou, Peng Xu, Zhenghua Xue, Weishi Zhang, Nader Asnafi
2018 MATEC Web of Conferences  
In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring.  ...  The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches  ...  for fraud detection.  ... 
doi:10.1051/matecconf/201818903002 fatcat:iri4vifvjzfpbgvof6qgqlfyvu

A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection [article]

Dinara Rzayeva, Saber Malekzadeh
2022 arXiv   pre-print
Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies.  ...  The main problem in credit card fraud detection is that the number of fraud transactions is significantly lower than genuine ones.  ...  Malini implemented KNN algorithm and outlier detection methods to optimize the best solution for the fraud detection problem; he showed that KNN could suit for detecting fraud with the limitation of memory  ... 
arXiv:2205.15300v1 fatcat:awgcc2eqn5h4zevct6qnquqa6y

Credit Card Fraud Detection using Deep Learning based on Neural Network and Auto-encoder

2020 International Journal of Engineering and Advanced Technology  
This paper discusses the performance analysis and the comparative study of the two Deep Learning algorithms which include auto-encoder and the neural network.  ...  Credit card fraud is an event problem and fraud detecting techniques getting more sophisticated each day. Mainly internet is becoming more common in almost every domain.  ...  Besides, various deep learning algorithms are used for detection of fraud, but in this paper, Neural Network and Auto-encoder is used to detect whether the usual data set transaction is eligible as new  ... 
doi:10.35940/ijeat.e9934.069520 fatcat:67pmckkxhjecdhe56adttiutpe

Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques

Marco Sánchez-Aguayo, Luis Urquiza-Aguiar, José Estrada-Jiménez
2022 Applied Sciences  
In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud.  ...  After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario.  ...  Marco Sánchez is a recipient of a teaching assistant fellowship from Escuela Politécnica Nacional for doctoral studies in computer science.  ... 
doi:10.3390/app12073382 fatcat:b4l7644t3fgvtic3mabsdmqxg4

Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection [article]

Ruinan Zhang, Fanglan Zheng, Wei Min
2018 arXiv   pre-print
In this paper, we propose an Recurrent Neural Netword (RNN) based deep-learning structure integrated with Markov Transition Field (MTF) for predicting online fraud behaviors using customer's interactions  ...  In practice, we tested and proved that the proposed network structure for processing sequential behavioral data could significantly boost fraud predictive ability comparing with the multilayer perceptron  ...  As a co-e ort of data scientists from e-commerce and nancial industry, we introduce a task-speci cal designed and engineered deep-learning network structure against online fraud a acks.  ... 
arXiv:1808.05329v1 fatcat:pm2wbr576zfwzdnlpj4prho65a

Handling Class Imbalance in Online Transaction Fraud Detection

Kanika, Jimmy Singla, Ali Kashif Bashir, Yunyoung Nam, Najam UI Hasan, Usman Tariq
2022 Computers Materials & Continua  
In this research work, an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning  ...  of the right thresholding method with deep learning yields better results.  ...  In this research work, a deep learning-based model has been proposed for handling class imbalance problem in online transaction fraud detection.  ... 
doi:10.32604/cmc.2022.019990 fatcat:jkpoeu7f7zbozpnrezuvqehfjy

DEEP LEARNING BASED HYBRID APPROACH OF DETECTING FRAUDULENT TRANSACTIONS

MIN JONG CHEON, DONG HEE LEE, HAN SEON JOO, OOK LEE
2021 Zenodo  
Through constructing a new model with a hybrid approach of deep learning and machine learning, which is composed of a Bi-LSTM-Autoencoder and Isolation Forest, we successfully detected fraudulent transactions  ...  Therefore, in order to solve this imbalance problem, we decided to construct a fraud detecting algorithm.  ...  We focused on fraud detection and tried several fraud detecting methods from machine learning models. The most representative models are Local Outlier and Isolation Forest.  ... 
doi:10.5281/zenodo.5393028 fatcat:c4vqx7tk6rfbzk7qx5km5d2uf4

Quick survey of graph-based fraud detection methods [article]

Paul Irofti, Andrei Patrascu, Andra Baltoiu
2021 arXiv   pre-print
We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes.  ...  In these networks, fraudulent behaviour may appear as a distinctive graph edge, such as spam message, a node or a larger subgraph structure, such as when a group of clients engage in money laundering schemes  ...  Further, in [21] , a novel fraud detection method is introduced based on deep learning.  ... 
arXiv:1910.11299v3 fatcat:zyupd4ezxrgw3f7g5utzihy6qi
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